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Software vulnerability detection is critical in software security because itidentifies potential bugs in software systems, enabling immediate remediationand mitigation measures to be implemented before they may be exploited.Automatic vulnerability identification is important because it can evaluatelarge codebases more efficiently than manual code auditing. Many MachineLearning (ML) and Deep Learning (DL) based models for detecting vulnerabilitiesin source code have been presented in recent years. However, a survey thatsummarises, classifies, and analyses the application of ML/DL models forvulnerability detection is missing. It may be difficult to discover gaps inexisting research and potential for future improvement without a comprehensivesurvey. This could result in essential areas of research being overlooked orunder-represented, leading to a skewed understanding of the state of the art invulnerability detection. This work address that gap by presenting a systematicsurvey to characterize various features of ML/DL-based source code levelsoftware vulnerability detection approaches via five primary research questions(RQs). Specifically, our RQ1 examines the trend of publications that leverageML/DL for vulnerability detection, including the evolution of research and thedistribution of publication venues. RQ2 describes vulnerability datasets usedby existing ML/DL-based models, including their sources, types, andrepresentations, as well as analyses of the embedding techniques used by theseapproaches. RQ3 explores the model architectures and design assumptions ofML/DL-based vulnerability detection approaches. RQ4 summarises the type andfrequency of vulnerabilities that are covered by existing studies. Lastly, RQ5presents a list of current challenges to be researched and an outline of apotential research roadmap that highlights crucial opportunities for futurework.

A Survey on Automated Software Vulnerability Detection Using Machine Learning and Deep Learning